Current-generation positron emission tomography (PET) scanners can acquire data as a 3D volume dataset, where lines of response at many axial and transverse angles are measured. An implementation of 3D filtered back-projection (FBP) is most commonly used to reconstruct these data. When applied to scanners with large axial acceptance angles, however, FBP can degrade the spatial resolution and lead to axial correlations in the edge slices. However, other reconstruction algorithms exist that may produce images with less bias and better spatial resolution than those created with FBP. The goal of this project is to investigate various implementations of the expectation maximization (EM) iterative reconstruction algorithm for 3D PET data, especially as applied to data acquired with a small-animal PET scanned The maximum likelihood EM (ML-EM) algorithm has been implemented on the Intel 1860 hypercube and more recently on the IBM SP2 parallel computers in the Division of Computer Research and Technology at NIH for 3D data acquired on both the small- and whole-animal PET system and the GE Medical Systems' Advance commercial body PET scanner. Initial results from simulated data from the small-animal scanner show a spatial resolution of <lmm with the ML-EM algorithm, compared with 1.5-2.0mm with FBP at comparable noise levels in the image. The results suggest that the EM algorithm makes better use of the higher sampling in the 3D PET data than FBP can. The next phase of this project will involve incorporation of corrections into the reconstruction algorithm for physical effects present in measured PET data, as well as a study of the resolution, accuracy, and noise properties of this algorithm. Also, we will be looking at other implementations of the EM algorithm in an effort to speed up the reconstruction without sacrificing accuracy or resolution.